DDID: a comprehensive resource for visualization and analysis of diet-drug interactions.

Diet-drug interactions (DDIs) are pivotal in drug discovery and pharmacovigilance. DDIs can modify the systemic bioavailability/pharmacokinetics of drugs, posing a threat to public health and patient safety. Therefore, it is crucial to establish a platform to reveal the correlation between diets and drugs. Accordingly, we have established a publicly accessible online platform, known as Diet-Drug Interactions Database (DDID, https://bddg.hznu.edu.cn/ddid/), to systematically detail the correlation and corresponding mechanisms of DDIs. The platform comprises 1338 foods/herbs, encompassing flora and fauna, alongside 1516 widely used drugs and 23 950 interaction records. All interactions are meticulously scrutinized and segmented into five categories, thereby resulting in evaluations (positive, negative, no effect, harmful and possible). Besides, cross-linkages between foods/herbs, drugs and other databases are furnished. In conclusion, DDID is a useful resource for comprehending the correlation between foods, herbs and drugs and holds a promise to enhance drug utilization and research on drug combinations.

Snowflake Cu2S@ZIF-67: A novel heterostructure substrate for enhanced adsorption and sensitive detection in BPA.

Developing semiconductor substrates with superior stability and sensitivity is challenging in surface-enhanced Raman scattering (SERS) research. Here, a snowflake Cu2S@ZIF-67 heterostructure was fabricated using a straightforward method, exhibiting a notable enhancement factor of 9.0 × 109 and a limit of detection (LOD) of 10-14 M for methylene blue (MB). In addition, the Cu2S@ZIF-67 heterostructure substrate demonstrates outstanding homogeneity (relative standard deviation (RSD) = 9.2%) and stability (120 days). Employing Cu2S generates highly sensitive hotspots via an electromagnetic (EM) mechanism, and the growth of ZIF-67 on its surface augments the adsorption capacity and charge transfer capability (chemical mechanism, CM), thereby enhancing the SERS detection sensitivity. Furthermore, the Cu2S@ZIF-67 heterostructure, which was used as a SERS substrate, facilitated the detection of bisphenol A (BPA) with an LOD of 10-11 M. The Cu2S@ZIF-67 heterostructure substrate has excellent selectivity and anti-interference, which is very suitable for BPA detection in complex environment applications. The accuracy of the Cu2S@ZIF-67 heterostructure as a SERS substrate for detecting BPA in real water samples (water bottles, tap water, and pure milk) was confirmed by comparison with high-performance liquid chromatography (HPLC). These results demonstrate that through the rational design of heterostructures can achieve the quantitative and accurate detection of hazardous substances in food and the environment can be achieved.

Engineering a Programmed Death-Ligand 1-Targeting Monobody Via Directed Evolution for SynNotch-Gated Cell Therapy.

Programmed death-ligand 1 (PD-L1) is a promising target for cancer immunotherapy due to its ability to inhibit T cell activation; however, its expression on various noncancer cells may cause on-target off-tumor toxicity when designing PD-L1-targeting Chimeric Antigen Receptor (CAR) T cell therapies. Combining rational design and directed evolution of the human fibronectin-derived monobody scaffold, "PDbody" was engineered to bind to PD-L1 with a preference for a slightly lower pH, which is typical in the tumor microenvironment. PDbody was further utilized as a CAR to target the PD-L1-expressing triple negative MDA-MB-231 breast cancer cell line. To mitigate on-target off-tumor toxicity associated with targeting PD-L1, a Cluster of Differentiation 19 (CD19)-recognizing SynNotch IF THEN gate was integrated into the system. This CD19-SynNotch PDbody-CAR system was then expressed in primary human T cells to target CD19-expressing MDA-MB-231 cancer cells. These CD19-SynNotch PDbody-CAR T cells demonstrated both specificity and efficacy in vitro, accurately eradicating cancer targets in cytotoxicity assays. Moreover, in an in vivo bilateral murine tumor model, they exhibited the capability to effectively restrain tumor growth. Overall, CD19-SynNotch PDbody-CAR T cells represent a distinct development over previously published designs due to their increased efficacy, proliferative capability, and mitigation of off-tumor toxicity for solid tumor treatment.

MOINER: A Novel Multiomics Early Integration Framework for Biomedical Classification and Biomarker Discovery.

In the context of precision medicine, multiomics data integration provides a comprehensive understanding of underlying biological processes and is critical for disease diagnosis and biomarker discovery. One commonly used integration method is early integration through concatenation of multiple dimensionally reduced omics matrices due to its simplicity and ease of implementation. However, this approach is seriously limited by information loss and lack of latent feature interaction. Herein, a novel multiomics early integration framework (MOINER) based on information enhancement and image representation learning is thus presented to address the challenges. MOINER employs the self-attention mechanism to capture the intrinsic correlations of omics-features, which make it significantly outperform the existing state-of-the-art methods for multiomics data integration. Moreover, visualizing the attention embedding and identifying potential biomarkers offer interpretable insights into the prediction results. All source codes and model for MOINER are freely available https://github.com/idrblab/MOINER.

Design superhydrophobic no-noble metal substrates for highly sensitive and signal stable SERS sensing.

Surface-enhanced Raman spectroscopy (SERS) is an analytical technique with a broad range of potential applications in fields such as biomedicine, electrochemistry, and hazardous chemicals. However, it is challenging to develop SERS substrates that are both good sensitive and signal stable. Here we designed a superhydrophobic Nd doped MoS2 uniformly assembled on the activated carbon fiber cloth (CFC) to avoid the coffee ring effect and enrich the analyte, improving the enhancement factor (EF) to 3.9 × 109 and pesticide endosulfan (<10-10) analyte detection. We demonstrate our strategy by density-functional theory (DFT) calculations confirming that both adsorption energy and density of states are enhanced after doping Nd leading to SERS enhancement. Beside DFT calculations, our experiments also provide an effective means to demonstrate that the high SERS sensitivity is based on multiple charge transfer processes combined with the activated carbon cloth. Our results address the limitations of low sensitivity and limit of detection (LOD) of semiconductor SERS substrates for trace analysis and are a step towards practical applications.

EMNPD: a comprehensive endophytic microorganism natural products database for prompt the discovery of new bioactive substances.

The discovery and utilization of natural products derived from endophytic microorganisms have garnered significant attention in pharmaceutical research. While remarkable progress has been made in this field each year, the absence of dedicated open-access databases for endophytic microorganism natural products research is evident. To address the increasing demand for mining and sharing of data resources related to endophytic microorganism natural products, this study introduces EMNPD, a comprehensive endophytic microorganism natural products database comprising manually curated data. Currently, EMNPD offers 6632 natural products from 1017 endophytic microorganisms, targeting 1286 entities (including 94 proteins, 282 cell lines, and 910 species) with 91 diverse bioactivities. It encompasses the physico-chemical properties of natural products, ADMET information, quantitative activity data with their potency, natural products contents with diverse fermentation conditions, systematic taxonomy, and links to various well-established databases. EMNPD aims to function as an open-access knowledge repository for the study of endophytic microorganisms and their natural products, thereby facilitating drug discovery research and exploration of bioactive substances. The database can be accessed at http://emnpd.idrblab.cn/ without the need for registration, enabling researchers to freely download the data. EMNPD is expected to become a valuable resource in the field of endophytic microorganism natural products and contribute to future drug development endeavors.

Association of tea and coffee consumption with the risk of all-cause and cause-specific mortality among individuals with metabolic syndrome: a prospective cohort study.

BACKGROUND: The relationship between tea and coffee consumption and mortality among patients with metabolic syndrome (MetS) remains barely explored. Herein, this study aimed to examine the association between tea and coffee consumption and the likelihood of all-cause and cause-specific mortality in patients with MetS. METHODS: A total of 118,872 participants with MetS at baseline from the UK Biobank cohort were included. Information on tea and coffee consumption was obtained during recruitment using a touchscreen questionnaire. Hazard ratios (HRs) and 95% confidence intervals (CIs) for mortality were determined using Cox proportional hazards models. RESULTS: During a median follow-up of 13.87 years, 13,666 deaths were recorded, with 5913, 3362, and 994 deaths from cancer, cardiovascular diseases (CVD), and respiratory disease (RD), respectively. This research showed a significant inverse association between tea intake and the risk of all-cause and cancer mortality, the respective HRs (95% CI) for consuming tea 2 vs. 0 cup/day were 0.89 (0.84-0.95), and 0.91 (0.83-0.99), and tea intake ≥ 4 cups/day could reduce CVD mortality by 11% (HR 0.89; 95% CI 0.81-0.98). The U-shaped nonlinear association between coffee intake and all-cause/CVD mortality was examined (all p-nonlinear < 0.001). The HRs (95% CI) for coffee consumption 1 vs. 0 cup/day were 0.93 (0.89-0.98) and 0.89 (0.80-0.99), and for ≥ 4 vs. 0 cup/day were 1.05 (1.01-1.11) and 1.13 (1.03-1.25), respectively. Notably, the combined intake of tea and coffee presented a protective effect against all-cause mortality (HR < 1). CONCLUSIONS: The importance of daily tea and moderate coffee consumption in individuals with MetS to optimise health benefits are highlighted.

Association of serum 25-hydroxyvitamin D with the incidence of 16 cancers, cancer mortality, and all-cause mortality among individuals with metabolic syndrome: a prospective cohort study.

PURPOSE: The relationship between vitamin D levels and cancer incidence and mortality in individuals with metabolic syndrome (MetS) remains poorly explored. Herein, we aimed to determine the association between 25-hydroxyvitamin D [25(OH)D] concentrations and the risk of 16 cancer incidence types and cancer/all-cause mortality in patients with MetS. METHODS: We enrolled 97,621 participants with MetS at recruitment from the UK Biobank cohort. The exposure factor was baseline serum 25(OH)D concentrations. The associations were examined using Cox proportional hazards models, which were displayed as hazard ratios (HRs) with 95% confidence intervals (CIs). RESULTS: Over a median follow-up period of 10.92 years for cancer incidence outcomes, 12,137 new cancer cases were recorded. We observed that 25(OH)D concentrations were inversely related to the risk of colon, lung, and kidney cancer, and HRs (95% CI) for 25(OH)D ≥ 75.0 vs. < 25.0 nmol/L were 0.67 (0.45-0.98), 0.64 (0.45-0.91), and 0.54 (0.31-0.95), respectively. The fully adjusted model revealed a null correlation between 25(OH)D and the incidence of stomach, rectum, liver, pancreas, breast, ovary, bladder, brain, multiple myeloma, leukemia, non-Hodgkin lymphoma, esophagus, and corpus uteri cancer. Over a median follow-up period of 12.72 years for mortality outcomes, 8286 fatalities (including 3210 cancer mortalities) were documented. An "L-shaped" nonlinear dose-response correlation was detected between 25(OH)D and cancer/all-cause mortality; the respective HRs (95% CI) were 0.75 (0.64-0.89) and 0.65 (0.58-0.72). CONCLUSION: These findings emphasize the importance of 25(OH)D in cancer prevention and longevity promotion among patients with MetS.

Systematic Description of the Content Variation of Natural Products (NPs): To Prompt the Yield of High-Value NPs and the Discovery of New Therapeutics.

Natural products (NPs) have long been associated with human production and play a key role in the survival of species. Significant variations in NP content may severely affect the "return on investment" of NP-based industries and render ecological systems vulnerable. Thus, it is crucial to construct a platform that relates variations in NP content to their corresponding mechanisms. In this study, a publicly accessible online platform, NPcVar (http://npcvar.idrblab.net/), was developed, which systematically described the variations of NP contents and their corresponding mechanisms. The platform comprises 2201 NPs and 694 biological resources, including plants, bacteria, and fungi, curated using 126 diverse factors with 26,425 records. Each record contains information about the species, NP, and factors involved, as well as NP content data, parts of the plant that produce NPs, the location of the experiment, and reference information. All factors were manually curated and categorized into 42 classes which belong to four mechanisms (molecular regulation, species factor, environmental condition, and combined factor). Additionally, the cross-links of species and NP to well-established databases and the visualization of NP content under various experimental conditions were provided. In conclusion, NPcVar is a valuable resource for understanding the relationship between species, factors, and NP contents and is anticipated to serve as a promising tool for improving the yield of high-value NPs and facilitating the development of new therapeutics.

A systematic review of state-of-the-art strategies for machine learning-based protein function prediction.

New drug discovery is inseparable from the discovery of drug targets, and the vast majority of the known targets are proteins. At the same time, proteins are essential structural and functional elements of living cells necessary for the maintenance of all forms of life. Therefore, protein functions have become the focus of many pharmacological and biological studies. Traditional experimental techniques are no longer adequate for rapidly growing annotation of protein sequences, and approaches to protein function prediction using computational methods have emerged and flourished. A significant trend has been to use machine learning to achieve this goal. In this review, approaches to protein function prediction based on the sequence, structure, protein-protein interaction (PPI) networks, and fusion of multi-information sources are discussed. The current status of research on protein function prediction using machine learning is considered, and existing challenges and prominent breakthroughs are discussed to provide ideas and methods for future studies.

A systematic review on the state-of-the-art strategies for protein representation.

The study of drug-target protein interaction is a key step in drug research. In recent years, machine learning techniques have become attractive for research, including drug research, due to their automated nature, predictive power, and expected efficiency. Protein representation is a key step in the study of drug-target protein interaction by machine learning, which plays a fundamental role in the ultimate accomplishment of accurate research. With the progress of machine learning, protein representation methods have gradually attracted attention and have consequently developed rapidly. Therefore, in this review, we systematically classify current protein representation methods, comprehensively review them, and discuss the latest advances of interest. According to the information extraction methods and information sources, these representation methods are generally divided into structure and sequence-based representation methods. Each primary class can be further divided into specific subcategories. As for the particular representation methods involve both traditional and the latest approaches. This review contains a comprehensive assessment of the various methods which researchers can use as a reference for their specific protein-related research requirements, including drug research.

CovInter: interaction data between coronavirus RNAs and host proteins.

Coronavirus has brought about three massive outbreaks in the past two decades. Each step of its life cycle invariably depends on the interactions among virus and host molecules. The interaction between virus RNA and host protein (IVRHP) is unique compared to other virus-host molecular interactions and represents not only an attempt by viruses to promote their translation/replication, but also the host's endeavor to combat viral pathogenicity. In other words, there is an urgent need to develop a database for providing such IVRHP data. In this study, a new database was therefore constructed to describe the interactions between coronavirus RNAs and host proteins (CovInter). This database is unique in (a) unambiguously characterizing the interactions between virus RNA and host protein, (b) comprehensively providing experimentally validated biological function for hundreds of host proteins key in viral infection and (c) systematically quantifying the differential expression patterns (before and after infection) of these key proteins. Given the devastating and persistent threat of coronaviruses, CovInter is highly expected to fill the gap in the whole process of the 'molecular arms race' between viruses and their hosts, which will then aid in the discovery of new antiviral therapies. It's now free and publicly accessible at: https://idrblab.org/covinter/.

Genetic risk, incident colorectal cancer, and the benefits of adhering to a healthy lifestyle: A prospective study using data from UK Biobank and FinnGen.

Background: Genetic factors increase the individual risk of colorectal cancer (CRC); however, the extent to which a healthy lifestyle can offset increased genetic risk is unknown. This study investigated whether a healthy lifestyle is associated with lower CRC risk, regardless of genetic risk. Methods: We recruited 390,365 participants without cancer at baseline (2006-2010) from the UK Biobank. The primary outcome was CRC incidence. A healthy lifestyle score constructed using 16 factors of six dimensions (smoking, drinking, body mass index, diet, exercise, and sleep) was categorized into three risk categories: favorable, intermediate, and unfavorable. To calculate the polygenic risk scores (PRSs) of UK Biobank participants, we extracted 454,678 single nucleotide polymorphisms (SNPs) from the UK Biobank and FinnGen Biobank after quality control. Cox proportional hazards regression was performed to evaluate the associations and was expressed as hazard ratios (HRs) with 95% confidence intervals (CIs). Results: During a median follow-up of 10.90 years, 4,090 new CRC cases were reported in the UK Biobank. The "best-fit" PRSs were constructed using 59 SNPs based on the UK Biobank cohort and FinnGen genome-wide association study summary data (R2 = 0.23%) and were divided into low (lowest quintile), intermediate (including second-fourth quintile), and high (highest quintile) genetic risk categories. The multivariate-adjusted Cox model revealed that participants with favorable lifestyles had HRs of 0.66 (95% CI = 0.60-0.72) for developing CRC vs. those with unfavorable lifestyles; low genetic risk was associated with a decreased risk of CRC (HR = 0.67, 95% CI =0.61-0.74) compared with those with high genetic risk. The HRs for low genetic risk participants with favorable lifestyles were 0.44 (95% CI =0.36-0.55) vs. participants with high genetic risk and unfavorable lifestyles. Among the participants with low, intermediate, or high genetic risk, the HRs of favorable vs. unfavorable lifestyles were 0.74, 0.64, and 0.72 (all p< 0.05). Conclusions: Low genetic risk and a favorable lifestyle were significantly associated with a decreased risk of CRC. A favorable lifestyle was associated with a lower CRC risk, regardless of genetic risk.

Association of sleep duration and noise exposure with hearing loss among Chinese and American adults: two cross-sectional studies.

OBJECTIVES: To examine the associations of sleep duration (SPD) and noise exposure with hearing loss (HL) among Chinese and American adults. DESIGN: Two cross-sectional studies. SETTING: The National Health and Nutrition Examination Survey (2011-2012), and Zhejiang Chinese participants between 1 January 2018 and 1 November 2021. PARTICIPANTS: 3322 adults from the USA and 4452 adults from Zhejiang, China. MAIN OUTCOME MEASURES: HL was defined as a pure-tone average >20 dB in the better ear at low frequency (500, 1000 and 2000 Hz), speech frequency (500, 1000, 2000 and 4000 Hz) or high frequency (3000, 4000, 6000 and 8000 Hz). Binary logistic regression analysis quantified the associations between SPD, noise exposure (at work or off-work) and HL. RESULTS: SPD ≥8 hours/night had an OR of 0.71 (95% CI 0.59 to 0.84) for high-frequency HL vs. an SPD of 6-8 hours/night among the Chinese participants but had an OR of 1.28 (95% CI 1.03 to 1.58) among American participants. Noise exposure (both at work and off-work) was associated with poorer low-frequency (OR 1.58, 1.43; p<0.05), speech-frequency (OR 1.63, 1.29; p<0.05) and high-frequency (OR 1.37, 1.23; p<0.05) hearing among the Chinese participants; and it was associated with worse high-frequency hearing (OR 1.43, 1.66; p<0.05) among the American participants. The negative relationship between SPD ≥8 hours/night and HL was mainly observed in the Chinese participants with noise exposure (OR <1, p<0.05), and SPD ≥8 hours/night associated with poorer HF hearing was only identified in the American participants without noise exposure (OR >1, p<0.05). CONCLUSIONS: Noise exposure was associated with poorer hearing. SPD ≥8 hours/night was negatively associated with HL in the Chinese participants especially when exposed to noise. SPD ≥8 hours/night was related to poorer high-frequency hearing in the American participants when they had no noise exposure.

Association of a Healthy Lifestyle with All-Cause, Cause-Specific Mortality and Incident Cancer among Individuals with Metabolic Syndrome: A Prospective Cohort Study in UK Biobank.

This study investigated the association between a healthy lifestyle with all-cause, cause-specific mortality, and cancer incidence among individuals with metabolic syndrome (MetS). Healthy lifestyle scores were created based on MetS management guidelines, including never/quitting smoking, moderate drinking, good sleep, healthy diet, sufficient exercise, social support, and less sedentary behaviour. Weighted healthy lifestyle scores were further constructed and classified into three groups: unfavourable (lowest quintile), intermediate (quintiles 2−4), and favourable (highest quintile) lifestyles. We included 87,342 MetS participants from the UK Biobank. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated using multivariate-adjusted Cox proportional hazards regression. During a median follow-up of 12.54 years, 6739 deaths were reported; during a median follow-up of 10.69 years, 10,802 new cancer cases were documented. We found a favourable lifestyle was inversely associated with all-cause mortality (HR: 0.57; 95%CI: 0.53−0.62), cause-specific mortality from respiratory disease, cancer, digestive disease, cardiovascular disease (HR < 1; p-trend < 0.001), and overall cancer incidence (HR: 0.84; 95% CI: 0.79−0.90). Our results indicate that adherence to healthy lifestyles is associated with lower overall cancer incidence and all-cause mortality risk among MetS individuals. However, causality cannot be made due to the nature of observational studies.

Monocytes engineered with iSNAP inhibit human B-lymphoma progression.

Monocytes are important regulators for the maintenance of homeostasis in innate and adaptive immune system and have been reported to play important role in cancer progression. CD47-SIRPα recognition is a coinhibitory immune signal to inhibit phagocytosis in monocytes and macrophages and has been well-known as the "Don't eat me" signal. By using an approach of integrated sensing and activating proteins (iSNAPs), we have rewired the CD47-SIRPα axis to create iSNAP-M which activates pathways in engineered human monocytes (iSNAP-MC). The mRNA expression levels of the monocyte/macrophage markers CD11b, CD14, and CD31 are upregulated in iSNAP-monocytes (iSNAP-MC). With PMA induction, the iSNAP-MC-derived macrophages (iSNAP-MΦ) showed upregelation in CD86 and CD80, but not CD206. TNFα expression and secretion were also increased in iSNAP-MΦ. Furthermore, the injection of iSNAP-MC into mice bearing human B-lymphoma tumors led to the suppression of tumor progression. Therefore, the engineered monocytes, via blockage of coinhibitory immune signals by rewiring CD47-SIRPα axis, can be applied to suppress target tumors for cancer immunotherapy.

Orthogonal array composite designs for drug combination experiments with applications for tuberculosis.

The aim of this article is to provide an overview of the orthogonal array composite design (OACD) methodology, illustrate the various advantages, and provide a real-world application. An OACD combines a two-level factorial design with a three-level orthogonal array and it can be used as an alternative to existing composite designs for building response surface models. We compare the D$$ D $$ -efficiencies of OACDs relative to the commonly used central composite design (CCD) when there are a few missing observations and demonstrate that OACDs are more robust to missing observations for two scenarios. The first scenario assumes one missing observation either from one factorial point or one additional point. The second scenario assumes two missing observations either from two factorial points or from two additional points, or from one factorial point and one additional point. Furthermore, we compare OACDs and CCDs in terms of I$$ I $$ -optimality for precise predictions. Lastly, a real-world application of an OACD for a tuberculosis drug combination study is provided.

Polydopamine-Induced Modification on the Highly Charged Surface of Asymmetric Nanofluidics: A Strategy for Adjustable Ion Current Rectification Properties.

Surface charge effects in nanoconfines is one of the fundamentals in the ion current rectification (ICR) of nanofluidics, which provides entropic driving force by asymmetric surface charges and causes ion enrichment/depletion by the electrostatic interaction of fixed surface charges. However, the surface charge effect causes a significant electrostatic repulsion in nanoconfines, restricting additional like charge or elaborate chemistry on the highly charged confined surface, which limits ICR manipulation. Here, we use polydopamine (PDA), a nearly universal adhesive, that adheres to the highly positive-charged poly(ethyleneimine) (PEI) gel network in a nanochannel array. PDA enhances the ICR effect from a low rectification ratio of 9.5 to 92.6 by increasing the surface charge and hydrophobicity of the PEI gel network and, meanwhile, shrinking its gap spacing. Theoretical and experimental results demonstrate the determinants of the fixed surface charge in the enrichment/depletion region on ICR properties, which is adjustable by PDA-induced change in a nanoconfined environment. Chemically active PDA brings Au nanoparticles by chloroauric reduction for further hydrophobization and the modification of negative-charged DNA complexes in nanochannels, whereby ICR effects can be manipulated in versatile means. The results describe an adjustable and versatile strategy for adjusting the ICR behaviors of nanofluidics by manipulating local surface charge effects using PDA.

Exponential Increase in an Ionic Signal: A Dominant Role of the Space Charge Effect on the Outer Surface of Nanochannels.

Nanochannels have advantage in sensitive analyses due to the confinement effects on ionic signal in nano- or sub-nanometric confines but could realize further gains by optimizing signal mechanism. Making target recognitions on the outer surface of nanochannels has been verified to improve target recognitions and signal conversions by maximizing surfaces accessible to targets and ions, but until recently, the signal mechanism has been still unclear. Using electroneutral peptide nucleic acid (PNA) and negative-charged DNA, we verified a dominant space charge effect on an ionic signal on the outer surface of nanochannels. A typical exponential increase of the ionic signal with the charge density on the outer surface has been demonstrated through the PNA-PNA, PNA-DNA, DNA-DNA hybrid, DNA cleavage, and hybridization chain reaction. These results challenge the essential role of steric hindrance on the ionic signal and describe a new ion passageway surrounded and accelerated by the stern layer of charged species on the nanochannel outer surface.

Revealing Ionic Signal Enhancement with Probe Grafting Density on the Outer Surface of Nanochannels.

Probe-modified nanopores/nanochannels are one of the most advanced sensors because the probes interact strongly with ions and targets in nanoconfinement and create a sensitive and selective ionic signal. Recently, ionic signals have been demonstrated to be sensitive to the probe-target interaction on the outer surface of nanopores/nanochannels, which can offer more open space for target recognition and signal conversion than nanoconfined cavities. To enhance the ionic signal, we investigated the effect of grafting density, a critical parameter of the sensing interface, of the probe on the outer surface of nanochannels on the change rate of the ionic signal before and after target recognition (ß). Electroneutral peptide nucleic acids and negatively charged DNA are selected as probes and targets, respectively. The experimental results showed that when adding the same number of targets, the ß value increased with the probe grafting density on the outer surface. A theoretical model with clearly defined physical properties of each probe and target has been established. Numerical simulations suggest that the decrease of the background current and the aggregation of targets at the mouth of nanochannels with increasing probe grafting density contribute to this enhancement. This work reveals the signal mechanism of probe-target recognition on the outer surface of nanochannels and suggests a general approach to the nanochannel/nanopore design leading to sensitivity improvement on the basis of relatively good selectivity.